code: def _generate_fake_inputs(N: int, H: int, W: int) -> Dict[str, Any]:
      R, T = look_at_view_transform(azim=torch.rand(N) * 360)
      return {
          "camera": PerspectiveCameras(R=R, T=T, device=DEVICE),
          "fg_probability": torch.randint(
              high=2, size=(N, 1, H, W), device=DEVICE
          ).float(),
          "depth_map": torch.rand((N, 1, H, W), device=DEVICE) + 0.1,
          "mask_crop": torch.randint(
              high=2, size=(N, 1, H, W), device=DEVICE
          ).float(),
          "sequence_name": ["sequence"] * N,
          "image_rgb": torch.rand((N, 1, H, W), device=DEVICE),
      }
  
file_path: pytorch3d/tests/implicitron/models/test_overfit_model.py
imports: import unittest
  from typing import Any, Dict
  from unittest.mock import patch
  import torch
  from pytorch3d.implicitron.models.generic_model import GenericModel
  from pytorch3d.implicitron.models.overfit_model import OverfitModel
  from pytorch3d.implicitron.models.renderer.base import EvaluationMode
  from pytorch3d.implicitron.tools.config import expand_args_fields
  from pytorch3d.renderer.cameras import look_at_view_transform, PerspectiveCameras
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_fake_inputs(N, H, W) and returns: dict